In 2015, there were 10.4 million cases of active tuberculosis (TB) and 1.4 million deaths due to TB. While we have effective TB treatment, the incidence of drug resistant cases is increasing and threatens TB control efforts. In 2015, 480,000 new cases of multi drug-resistant TB (MDR-TB) were reported. Among these, nearly 60,000 were extensively drug-resistant TB (XDR-TB). The scenario is much worse in some regions. In Belarus, close to one of each two cases (48%) are MDR-TB (35.3% in new and 76.5% in previously treated TB-patients). Since the global cure rate of MDR-TB is still well below the target set by WHO for 2015 (exceedingly low in many parts of the world), and since XDR-TB treatment success rates are even lower (20% in Belarus), there is an urgent need for improved understanding of the problem and to identify/evaluate new drugs/combinations of drugs as the situation in Belarus is likely to spread. Two trials for combinatorial treatment involving four new and repurposed drugs bedaquiline, linezolid, clofazimine, and delamanid are underway thanks to funding from the World Health Organization and the Global Fund. As part of these two trials, monthly sputum samples will be collected for six months from all patients. Unfortunately, in the first trial with 30 patients having completed the combinatorial treatment, in six the treatment has failed, and one death has been recorded. This project aims to leverage the resources created by the two trials in order to uncover previously unknown mechanisms of drug resistance, evolutionary path to resistance, and timeline to resistance to the four new/repurposed drugs. Our approach will be to use in silico comparative genomic and epigenetic (methylome and transcriptomic) analysis in order to curate a comprehensive catalog of uncharacterized (epi)genomic changes in failed treatment cases. In silico functional characterization of genes and regulatory elements associated with resistance to the five study drugs will then elucidate the mechanism of resistance. A subsequent phylogenomic analysis and MIC characterization of time-course samples will allow us to understand the evolutionary path to resistance, and the change in resistance level after each evolutionary event. This will allow us to understand for example whether resistance emerges in steps with increasing levels, or spontaneously at a high level. In the case of the former, we will be able to identify the stepwise genetic and methylation markers associated with each resistance level. This allows the clinician to decide whether increasing the drug dosage or change of the drug regimen is the best course of action.